{"id":33270,"date":"2025-06-24T16:53:03","date_gmt":"2025-06-24T14:53:03","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=33270"},"modified":"2025-06-30T09:28:04","modified_gmt":"2025-06-30T07:28:04","slug":"domina-el-chain-of-thought-prompting","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/domina-el-chain-of-thought-prompting\/","title":{"rendered":"Domina el Chain-of-Thought Prompting"},"content":{"rendered":"\n<p>Hemos visto c\u00f3mo los Large Language Models (<a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/ingenieria-de-prompts-y-el-potencial-oculto-de-los-llms\/\">LLMs<\/a>) son capaces de generar textos asombrosos, pero a veces, cuando les presentamos problemas complejos, sus respuestas pueden parecer directas y carentes de la l\u00f3gica interna que esperar\u00edamos de un razonamiento humano. Aqu\u00ed es donde entra en juego una t\u00e9cnica revolucionaria: el <strong><em>CoT<\/em><\/strong>, o Inducci\u00f3n de la Cadena de Pensamiento.<\/p>\n\n\n\n<p>Imagina intentar resolver un acertijo complejo. \u00bfSimplemente adivinar\u00edas la respuesta? Probablemente no. Desglosar\u00edas el problema en pasos m\u00e1s peque\u00f1os, analizar\u00edas la informaci\u00f3n disponible, considerar\u00edas diferentes posibilidades y, gradualmente, construir\u00edas un camino l\u00f3gico hacia la soluci\u00f3n. El <strong><em>chain-of-thought prompting<\/em><\/strong> busca emular este proceso de razonamiento dentro de los LLMs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-que-es-el-chain-of-thought-prompting\"><strong>\u00bfQu\u00e9 es el Chain-of-Thought Prompting?<\/strong><\/h2>\n\n\n\n<p>Es una t\u00e9cnica en la que se invita al modelo de lenguaje a razonar de forma secuencial, expresando cada paso de su pensamiento antes de llegar a la respuesta final. En lugar de pedir solo una soluci\u00f3n, se solicita que el modelo explique su proceso: identificando datos relevantes, aplicando f\u00f3rmulas o principios, y conectando las ideas de manera l\u00f3gica. De este modo, se obtiene no solo el resultado, sino tambi\u00e9n una visi\u00f3n de c\u00f3mo se alcanz\u00f3 la respuesta, lo que es especialmente \u00fatil en problemas que requieren razonamiento complejo o varios pasos para resolverse.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-como-funciona-el-nbsp-cot\"><strong>\u00bfC\u00f3mo Funciona el&nbsp;CoT?<\/strong><\/h2>\n\n\n\n<p>En esencia, en lugar de pedirle directamente al <em>LLM<\/em> la respuesta a un problema complejo, le pedimos que <em>muestre su proceso de pensamiento<\/em>. Le solicitamos que explique los pasos intermedios, las consideraciones y la l\u00f3gica que lo llevan a su conclusi\u00f3n. Al hacerlo, desbloqueamos una capacidad de razonamiento mucho m\u00e1s profunda y confiable en el modelo.<\/p>\n\n\n\n<p>Consideremos un ejemplo simplificado:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><em><a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/ingeniero-de-prompts-mas-alla-del-termino-de-moda\/\">Prompt <\/a>Tradicional: <\/em><\/strong>\u00bfQu\u00e9 n\u00famero viene despu\u00e9s de 2, 4, 8?<\/li>\n\n\n\n<li><strong><em>Respuesta del LLM (potencialmente directa): <\/em><\/strong><em>16<\/em><\/li>\n<\/ul>\n\n\n\n<p>Si bien la respuesta es correcta, no obtenemos ninguna informaci\u00f3n sobre <em>c\u00f3mo<\/em> lleg\u00f3 a esa conclusi\u00f3n.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><em>Prompt con Chain-of-Thought: <\/em><\/strong>Explica tu razonamiento paso a paso para determinar qu\u00e9 n\u00famero viene despu\u00e9s de 2, 4, 8.<\/li>\n\n\n\n<li><strong><em>Respuesta del LLM (con Chain-of-Thought): <\/em><\/strong>El primer n\u00famero es 2. El segundo n\u00famero es 4, que es 2 multiplicado por 2. El tercer n\u00famero es 8, que es 4 multiplicado por 2 (o 2 multiplicado por 2 multiplicado por 2). Por lo tanto, la secuencia parece estar multiplicando el n\u00famero anterior por 2. Siguiendo esta l\u00f3gica, el siguiente n\u00famero deber\u00eda ser 8 multiplicado por 2, que es 16.<\/li>\n<\/ul>\n\n\n\n<p>\u00a1La diferencia es notable! Con el <em>CoT<\/em>, no solo obtenemos la respuesta, sino tambi\u00e9n la <em>justificaci\u00f3n<\/em> detr\u00e1s de ella. Esto nos permite evaluar si el razonamiento del LLM es correcto y comprender mejor c\u00f3mo aborda el problema.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;ingenier\u00eda de prompts&quot;\" href=\"https:\/\/pikaso.cdnpk.net\/private\/production\/1960791339\/conversions\/render-preview.jpg?token=exp=1762214400~hmac=ca6d25eb4c2f8bbdbaf559b8fac38e0b646455dddebbd9494846701e6e97d7e0\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*qTGoPLxi1ajJ-71N0I8nzQ.jpeg\" alt=\"\"\/><\/a><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-tecnicas-para-inducir-la-cadena-de-pensamiento\"><strong><em>T\u00e9cnicas para Inducir la Cadena de Pensamiento<\/em><\/strong><\/h3>\n\n\n\n<p>Existen varias maneras de implementar el <em>chain-of-thought prompting<\/em>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><em>Let\u2019s think step by step<\/em>:<\/strong> Esta frase simple pero poderosa al inicio del prompt a menudo es suficiente para alentar al <em>LLM<\/em> a explicitar su razonamiento.<\/li>\n\n\n\n<li><strong><em>Preguntas Gu\u00eda<\/em>:<\/strong> Podemos intercalar preguntas que gu\u00eden el proceso de pensamiento del modelo. Por ejemplo, ante un problema de inferencia, podr\u00edamos preguntar \u201c\u00bfQu\u00e9 informaci\u00f3n clave se nos proporciona?\u201d, \u201c\u00bfQu\u00e9 podemos inferir de esta informaci\u00f3n?\u201d, \u201c\u00bfC\u00f3mo se relacionan estas inferencias para llegar a una conclusi\u00f3n?\u201d.<\/li>\n\n\n\n<li><strong><em>Few-Shot Prompting con CoT<\/em>:<\/strong> Proporcionar ejemplos en el prompt donde se muestre expl\u00edcitamente la cadena de pensamiento para problemas similares puede ense\u00f1ar al LLM a razonar de la misma manera. Estos ejemplos act\u00faan como un \u201centrenamiento\u201d dentro del propio prompt.<\/li>\n\n\n\n<li><strong><em>Descomposici\u00f3n del Problema<\/em>:<\/strong> Si el problema es complejo, podemos pedirle al LLM que lo descomponga en subproblemas m\u00e1s peque\u00f1os y que resuelva cada uno paso a paso antes de llegar a la soluci\u00f3n final.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-por-que-es-tan-importante-el-chain-of-thought\"><strong><em>\u00bfPor qu\u00e9 es tan Importante el Chain-of-Thought?<\/em><\/strong><\/h3>\n\n\n\n<p>El CoT ofrece m\u00faltiples beneficios:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Mejora la Precisi\u00f3n en Tareas Complejas:<\/strong> Para problemas que requieren l\u00f3gica, inferencia o planificaci\u00f3n, el <em>CoT<\/em> puede aumentar significativamente la exactitud de las respuestas.<\/li>\n\n\n\n<li><strong>Mayor Interpretabilidad:<\/strong> Al ver el proceso de pensamiento del <em>LLM<\/em>, podemos entender mejor c\u00f3mo llega a sus conclusiones, lo que aumenta la transparencia y la confianza en sus resultados.<\/li>\n\n\n\n<li><strong>Depuraci\u00f3n y Correcci\u00f3n de Errores:<\/strong> Si la respuesta final es incorrecta, analizar la cadena de pensamiento puede ayudarnos a identificar d\u00f3nde fall\u00f3 el razonamiento y a ajustar el prompt para guiarlo mejor.<\/li>\n\n\n\n<li><strong>Fomenta un Razonamiento M\u00e1s \u201cHumano\u201d:<\/strong> Al emular el proceso de pensamiento paso a paso, los <em>LLMs<\/em> pueden generar respuestas que se sienten m\u00e1s intuitivas y l\u00f3gicas para nosotros. Desglosar el problema en pasos evita errores de c\u00e1lculo o deducci\u00f3n, ya que se pueden detectar y corregir posibles fallos en cada etapa.<\/li>\n\n\n\n<li><strong>Transparencia:<\/strong> Permite que el usuario entienda el proceso interno de la IA, lo que aporta confianza y la posibilidad de afinar el prompting para problemas espec\u00edficos.<\/li>\n\n\n\n<li><strong>Versatilidad:<\/strong> Esta t\u00e9cnica es aplicable a tareas de matem\u00e1ticas, l\u00f3gica, toma de decisiones y m\u00e1s, ya que optimiza la claridad y coherencia en respuestas complejas.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;Prompts\/IA&quot;\" href=\"https:\/\/gemini.google.com\/app\/716da52913ea3e35?hl=es\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*TsVKukqdnQZSRkIEIsKNLg.png\" alt=\"\"\/><\/a><\/figure>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\" id=\"h-ejemplos-practicos\"><strong><em>Ejemplos Pr\u00e1cticos<\/em><\/strong><\/h4>\n\n\n\n<p>A continuaci\u00f3n, se muestran dos ejemplos que ilustran c\u00f3mo usar <em>chain-of-thought prompting<\/em> utilizando la API de OpenAI.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><em>Ejemplo Problema Matem\u00e1tico Simple <\/em><\/strong>En este primer ejemplo, se solicita al modelo que resuelva un problema matem\u00e1tico detallando cada paso del proceso.<\/li>\n<\/ul>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">import openai\n\n<span class=\"hljs-comment\"># Configurar la clave API<\/span>\nopenai.api_key = <span class=\"hljs-string\">\"TU_API_KEY\"<\/span>\n<span class=\"hljs-comment\"># Definici\u00f3n del prompt con instrucciones de chain-of-thought<\/span>\nprompt = (\n    <span class=\"hljs-string\">\"Resuelve el siguiente problema matem\u00e1tico explicando paso a paso tu razonamiento:\\n\"<\/span>\n    <span class=\"hljs-string\">\"Problema: Un tren recorre 300 kil\u00f3metros en 3 horas. \u00bfCu\u00e1l es la velocidad promedio del tren?\\n\\n\"<\/span>\n    <span class=\"hljs-string\">\"Instrucciones:\\n\"<\/span>\n    <span class=\"hljs-string\">\"1. Identifica la f\u00f3rmula para calcular la velocidad promedio.\\n\"<\/span>\n    <span class=\"hljs-string\">\"2. Sustituye los valores correspondientes en la f\u00f3rmula.\\n\"<\/span>\n    <span class=\"hljs-string\">\"3. Realiza el c\u00e1lculo y explica el resultado obtenido.\\n\"<\/span>\n    <span class=\"hljs-string\">\"Respuesta:\"<\/span>\n)\nresponse = openai.ChatCompletion.create(\n    model=<span class=\"hljs-string\">\"gpt-4\"<\/span>,\n    messages=&#91;\n        {<span class=\"hljs-string\">\"role\"<\/span>: <span class=\"hljs-string\">\"system\"<\/span>, <span class=\"hljs-string\">\"content\"<\/span>: <span class=\"hljs-string\">\"Eres un experto en matem\u00e1ticas y en explicar procesos de razonamiento paso a paso.\"<\/span>},\n        {<span class=\"hljs-string\">\"role\"<\/span>: <span class=\"hljs-string\">\"user\"<\/span>, <span class=\"hljs-string\">\"content\"<\/span>: prompt}\n    ]\n)\n<span class=\"hljs-keyword\">print<\/span>(response.choices&#91;<span class=\"hljs-number\">0<\/span>].message.content)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong><em>Explicaci\u00f3n:&nbsp;<\/em><\/strong><\/p>\n\n\n\n<p>Se configura el mensaje inicial indicando al modelo que act\u00fae como un experto en matem\u00e1ticas y que detalle su razonamiento. El prompt est\u00e1 estructurado para guiar al modelo a identificar la f\u00f3rmula (<strong><em>velocidad = distancia\/tiempo<\/em><\/strong>), aplicar los datos y detallar cada paso. Al obtener la respuesta, se visualizar\u00e1 la cadena de pensamiento, donde el modelo explica c\u00f3mo llega al resultado final (en este caso, una velocidad de 100 km\/h).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><em>Ejemplo Problema Complejo con M\u00faltiples Pasos y Datos Mixtos: <\/em><\/strong>En situaciones donde se debe abordar problemas m\u00e1s complejos, es muy \u00fatil incluir ejemplos que muestren c\u00f3mo se debe estructurar el razonamiento.<\/li>\n<\/ul>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript\"><span class=\"hljs-keyword\">import<\/span> openai\n\nopenai.api_key = <span class=\"hljs-string\">\"TU_API_KEY\"<\/span>\nmessages = &#91;\n    {<span class=\"hljs-string\">\"role\"<\/span>: <span class=\"hljs-string\">\"system\"<\/span>, <span class=\"hljs-string\">\"content\"<\/span>: (\n        <span class=\"hljs-string\">\"Eres un experto en resoluci\u00f3n de problemas complejos. Utiliza la t\u00e9cnica chain-of-thought para \"<\/span>\n        <span class=\"hljs-string\">\"explicar cada etapa de tu razonamiento de forma clara y detallada.\"<\/span>\n    )},\n    {<span class=\"hljs-string\">\"role\"<\/span>: <span class=\"hljs-string\">\"user\"<\/span>, <span class=\"hljs-string\">\"content\"<\/span>: (\n        <span class=\"hljs-string\">\"Ejemplo demostrativo:\\n\"<\/span>\n        <span class=\"hljs-string\">\"Sup\u00f3n la siguiente situaci\u00f3n: Una empresa tiene diferentes departamentos que presentan distintos \"<\/span>\n        <span class=\"hljs-string\">\"resultados trimestrales. Se requiere analizar c\u00f3mo el incremento del presupuesto en marketing \"<\/span>\n        <span class=\"hljs-string\">\"est\u00e1 afectando las ventas totales.\\n\\n\"<\/span>\n        <span class=\"hljs-string\">\"Pasos sugeridos:\\n\"<\/span>\n        <span class=\"hljs-string\">\"1. Identificar los departments y sus datos de ventas.\\n\"<\/span>\n        <span class=\"hljs-string\">\"2. Relacionar el gasto en marketing con las variaciones en ventas.\\n\"<\/span>\n        <span class=\"hljs-string\">\"3. Evaluar si existe correlaci\u00f3n entre el incremento del presupuesto y el aumento de las ventas.\\n\\n\"<\/span>\n        <span class=\"hljs-string\">\"Ahora, resuelve el siguiente problema utilizando chain-of-thought:\\n\"<\/span>\n        <span class=\"hljs-string\">\"Problema: En una campa\u00f1a publicitaria, se invirtieron 5000 USD, lo que se tradujo en un aumento de 150 unidades \"<\/span>\n        <span class=\"hljs-string\">\"vendidas, cada una a 20 USD. Explica c\u00f3mo estimar el retorno de inversi\u00f3n (ROI) de esta acci\u00f3n, detallando cada paso.\"<\/span>\n    )}\n]\nresponse = openai.ChatCompletion.create(\n    model=<span class=\"hljs-string\">\"gpt-4\"<\/span>,\n    messages=messages\n)\nprint(response.choices&#91;<span class=\"hljs-number\">0<\/span>].message.content)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong><em>Explicaci\u00f3n:<\/em><\/strong><\/p>\n\n\n\n<p>Se inicia indicando un contexto amplio para que el modelo act\u00fae como experto en an\u00e1lisis de datos y resoluci\u00f3n de problemas. El usuario presenta un ejemplo detallado y luego plantea un nuevo problema. El prompt especifica que se explique el proceso de c\u00e1lculo del <em>ROI<\/em>, lo cual fuerza al modelo a desglosar la respuesta en pasos (por ejemplo, definici\u00f3n de ROI, c\u00e1lculo de beneficios y comparaci\u00f3n con la inversi\u00f3n). La respuesta detallada ayudar\u00e1 a visualizar c\u00f3mo el modelo enlaza cada paso para llegar a la conclusi\u00f3n final.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-text-align-center\" id=\"h-mejores-practicas-para-aplicar-chain-of-thought\"><strong>Mejores Pr\u00e1cticas para Aplicar Chain-of-Thought<\/strong><\/h3>\n\n\n\n<p>Para lograr los mejores resultados al utilizar <em>chain-of-thought prompting<\/em>, ten en cuenta estas recomendaciones:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>S\u00e9 Espec\u00edfico:<\/strong> Define claramente los pasos que esperas que el modelo siga. Esto ayuda a evitar respuestas resumidas o inexactas.<\/li>\n\n\n\n<li><strong>Proporciona Ejemplos:<\/strong> Utiliza un enfoque <em>few-shot<\/em> donde incluyas ejemplos claros de c\u00f3mo debe estructurarse el razonamiento.<\/li>\n\n\n\n<li><strong>Establece un Contexto Adecuado:<\/strong> Aseg\u00farate de que el mensaje de sistema posicione al modelo en el rol adecuado, de modo que interprete correctamente la solicitud.<\/li>\n\n\n\n<li><strong>Itera y Refinar:<\/strong> No dudes en ajustar y probar m\u00faltiples versiones del prompt hasta lograr la respuesta deseada.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading has-text-align-center\" id=\"h-el-futuro-del-razonamiento-en-la-nbsp-ia\"><strong>El Futuro del Razonamiento en la&nbsp;IA<\/strong><\/h3>\n\n\n\n<p>El <em>chain-of-thought prompting<\/em> representa un avance crucial en nuestra interacci\u00f3n con los <em>LLMs<\/em>. Nos movemos de simplemente obtener respuestas a comprender el <em>proceso<\/em> detr\u00e1s de ellas. A medida que esta t\u00e9cnica contin\u00faa desarroll\u00e1ndose, podemos esperar que los <em>LLMs<\/em> se vuelvan a\u00fan m\u00e1s h\u00e1biles para abordar problemas complejos, explicar sus decisiones y colaborar con nosotros de maneras m\u00e1s significativas y transparentes.<\/p>\n\n\n\n<p>El <em>chain-of-thought prompting<\/em> abre un abanico de posibilidades en la interacci\u00f3n con los modelos de lenguaje. Al obligar al modelo a desplegar su razonamiento paso a paso, se mejora la precisi\u00f3n en tareas complejas y se incrementa la transparencia en el proceso de generaci\u00f3n de respuestas. Ya sea para resolver problemas matem\u00e1ticos o analizar datos complejos, esta t\u00e9cnica fomenta un di\u00e1logo m\u00e1s profundo y preciso con la inteligencia artificial.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;chain-of-thought prompting&quot;\" href=\"https:\/\/gemini.google.com\/app\/f334a6111629b320?hl=es\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*Mmjj9guqPmWLdDgyRdry6A.png\" alt=\"\"\/><\/a><\/figure>\n<\/div>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hemos visto c\u00f3mo los Large Language Models (LLMs) son capaces de generar textos asombrosos, pero a veces, cuando les presentamos problemas complejos, sus respuestas pueden parecer directas y carentes de la l\u00f3gica interna que esperar\u00edamos de un razonamiento humano. Aqu\u00ed es donde entra en juego una t\u00e9cnica revolucionaria: el CoT, o Inducci\u00f3n de la Cadena&#8230; <a class=\"more-link\" href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/domina-el-chain-of-thought-prompting\/\">Read more<\/a><\/p>\n","protected":false},"author":313,"featured_media":30224,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[10642,10598],"tags":[13351,12083],"collections":[],"class_list":{"0":"post-33270","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-deep-learning-es","8":"category-inteligencia-artificial","9":"tag-prompting-engineering","10":"tag-prompts","11":"entry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin 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